Korpe, Ugur UfukGokdag, MustafaGulbudak, Ozan2024-09-292024-09-292024979-8-3503-5108-8979-8-3503-5109-52832-7667https://doi.org/10.1109/GPECOM61896.2024.10582723https://hdl.handle.net/20.500.14619/61296th Global Power, Energy and Communication Conference (GPECOM) -- JUN 04-07, 2024 -- Budapest, HUNGARYInduction machines (IM) are still widely used in the industry due to their advantages, such as low maintenance requirements and improved robustness. The field-oriented control (FOC), direct torque control (DTC), and model predictive control (MPC) techniques are used to control IM in high-performance control applications. The common disadvantage of these control techniques is that the control performances are negatively affected by changes in machine parameters, and machine parameters vary non-linearly depending on the magnetic saturation and temperature. To solve this negative affect, the control technique can be optimized by using a parameter estimation methods. Another solution to eliminate these negative effects is to design a reinforcement learning (RL)-based controller that regulates the control variables without the knowledge of machine parameters. In this study, IM speed control is performed using a twin-delayed deep deterministic policy gradient (TD3) agent. The dynamic and steady-state performance of the designed controller are compared with the traditional control techniques. Extensive simulation results have shown that the dynamic and steady-state performance of the designed controller is better than other control techniques.eninfo:eu-repo/semantics/closedAccessInduction motorparameter estimationreinforcement learningTD3 agentConference Object10.1109/GPECOM61896.2024.105827232-s2.0-85199041185184N/A179WOS:001268516300112N/A